Riccardo Bravin

This paper presents a study of an automated system for identifying planktic foraminifera at the species level. The system uses a combination of deep learning methods, specifically convolutional neural networks (CNNs), to analyze digital images of foraminifera taken at different illumination angles. The dataset is composed of 1437 groups of sixteen grayscale images, one group for each foraminifera specimen, that are then converted to RGB images with various processing methods. These RGB images are fed into a set of CNNs, organized in an ensemble learning (EL) environment. The ensemble is built by training different networks using different approaches for creating the RGB images. The study finds that an ensemble of CNN models trained on different RGB images improves the system’s performance compared to other state-of-the-art approaches. The main focus of this paper is to introduce multiple colorization methods that differ from the current cutting-edge techniques; novel strategies like Gaussian or mean-based techniques are suggested. The proposed system was also found to outperform human experts in classification accuracy.

This paper presents a study of an automated system for identifying planktic foraminifera at the species level. The system uses a combination of deep learning methods, specifically Convolutional Neural Networks (CNNs), to analyze digital images of foraminifera taken at different illumination angles. The dataset is composed of 1437 groups of sixteen grayscale images, one group for each foraminifer, that are then converted to RGB images with various processing methods. These RGB images are fed into a set of CNNs, organized in an Ensemble Learning (EL) environment. The ensemble is built by training different networks using different approaches for creating the RGB images. The study finds that an ensemble of CNN models trained on different RGB images improves the system's performance compared to other state-of-the-art approaches. The proposed system was also found to outperform human experts in classification accuracy.

Convolutional Neural Networks (CNNs) are used in many domains but the requirement of large datasets for robust training sessions and no overfitting makes them hard to apply in medical fields and similar fields. However, when large quantities of samples cannot be easily collected, various methods can still be applied to stem the problem depending on the sample type. Data augmentation, rather than other methods, has recently been under the spotlight mostly because of the simplicity and effectiveness of some of the more adopted methods. The research question addressed in this work is whether data augmentation techniques can help in developing robust and efficient machine learning systems to be used in different domains for classification purposes. To do that, we introduce new image augmentation techniques that make use of different methods like Fourier Transform (FT), Discrete Cosine Transform (DCT), Radon Transform (RT), Hilbert Transform (HT), Singular Value Decomposition (SVD), Local Laplacian Filters (LLF) and Hampel filter (HF). We define different ensemble methods by combining various classical data augmentation methods with the newer ones presented here. We performed an extensive empirical evaluation on 15 different datasets to validate our proposal. The obtained results show that the newly proposed data augmentation methods can be very effective even when used alone. The ensembles trained with different augmentations methods can outperform some of the best approaches reported in the literature as well as compete with state-of-the-art custom methods. All resources are available at https://github.com/LorisNanni.